Collaborating Authors

Machine Learning for Music Recommendation


This approach tends to teach to machines what humans like to listen to, without understanding what is recommended. It is a deaf approach that's trying to mimic the record dealer's behavior. It's not a DJ that builds a listening experience. It doesn't capture what the soundtrack of your life is. Collaborative filtering also tends to make predictable and familiar recommendations.

Defining a machine learning problem


What we are looking at is a recommendation engine problem. Given the personal data you want to make the diet recommendation that are best suited for the person based on the data inserted.

e-Book: Machine Learning and Recommendation Engine


Building a simple but powerful recommendation system is much easier than you think. This guide explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommender. In this guide, Practical Machine Learning: Innovations in Recommendation, authors and Mahout committers Ted Dunning and Ellen Friedman shed light on a more approachable recommendation engine design and the business advantages for leveraging this innovative implementation style.

How to Build a Product Recommendation System. Machine Learning Solutions


One pressing issue of product recommendation systems today is the scalability of algorithms with large, real-world datasets. It's possible that a recommendation algorithm will work well and produce accurate results with small datasets, yet may start producing inaccurate or inefficient results with large ones. In addition, some algorithms are computationally expensive to run – the larger the dataset, the longer it will take, and the more it will cost the business to analyse and make recommendations from it. Advanced, large-scale assessment methods are required to deal with both issues.

Towards Deep Conversational Recommendations

Neural Information Processing Systems

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations.